Methods and apparatus for system identification

ABSTRACT

A method of identifying a system, the method comprising: obtaining an indication of background noise present at the system; generating a probe signal based on the indication; applying the probe signal to the system; estimating a response of the system to the probe signal; and identifying the system based on the measured response and the probe signal, wherein the probe signal comprises a whitening component configured to whiten noise in the estimated response due to the background noise present at the system.

TECHNICAL FIELD

Embodiments of the present disclosure relate to methods, apparatus andsystems for system identification, for example for biometric processes.

BACKGROUND

It is known that the acoustic properties of a user's ear, whether theouter parts (known as the pinna or auricle), the ear canal or both,differ substantially between individuals and can therefore be used as abiometric to identify the user. One or more loudspeakers or similartransducers positioned close to or within the ear generate an acousticstimulus, and one or more microphones or other transducers similarlypositioned close to or within the ear detect the acoustic response ofthe ear to the acoustic stimulus. The response may be an ear canalimpulse response (ECIR) or an ear canal frequency response (ECFR). Oneor more features may be extracted from the response signal and used tocharacterize an individual.

For example, the ear canal is a resonant system, and therefore onefeature which may be extracted from the response signal is the impulseresponse or frequency response of the ear canal (ECIR or ECFR). If themeasured resonant frequency (i.e. in the response signal) differs from astored resonant frequency for the user, a biometric algorithm coupled toreceive and analyse the response signal may return a negative result.Other features of the response signal may be similarly extracted andused to characterize the individual. For example, the features maycomprise one or more mel frequency cepstrum coefficients. Moregenerally, the transfer function between the acoustic stimulus and themeasured response signal (or features of the transfer function) may bedetermined and compared to a stored transfer function (or storedfeatures of the transfer function) which is characteristic of the user.

One problem associated with ear biometric systems is that the signal tonoise ratio of the measured response signal is typically quite low asthe biometric features of the signal are relatively weak. This problemcan be exacerbated depending on a number of factors. For example, theuser may be present in a noisy environment. For example, earphones usedto acquire the ear biometric data may be poorly fitted to the user's ear(e.g. inserted too far into the user's ear, or not sufficientlyinserted). For example, the user may be generating noise in the canal orheadset due to own voice, chewing sounds and handling of the headset. Toimprove signal-to-noise ratio (SNR) and to ensure as many frequencymodes as possible of ear canals of a population group are excited, probesignals for biometrics typically comprise white noise. However, whitenoise tends to be perceptually unpleasant to the human ear. SNR may alsobe improved by increasing the level of the probe signal. However, highlevel probe signals tend to be intrusive and harsh to the human ear.

Ear canal characterisation can also be key to adaptive active noisecancellation (ANC) systems. ANC performance depends on the accurateestimation of ear canal response. The accuracy of the estimation inhigh-level background noise conditions can be improved by appropriateselection of the playback signal used to stimulate the ear canal system.

SUMMARY

According to a first aspect of the disclosure, there is provided amethod of identifying a system, the method comprising: obtaining anindication of background noise present at the system; generating a probesignal based on the indication; applying the probe signal to the system;estimating a response of the system to the probe signal; and identifyingthe system based on the measured response and the probe signal, whereinthe probe signal comprises a whitening component configured to whitennoise in the estimated response due to the background noise present atthe system.

The probe signal may be an acoustic stimulus for use in an acousticprocess on a user and the system comprises an ear canal of the user. Theacoustic process may comprise active noise cancellation (ANC). The probesignal may be an audio playback signal.

The estimated response may represent a combination of a leakage patharound a personal audio device worn by the user and a response of theear canal to probe signal. For example, the leakage path may be anacoustic path between the inside of the ear canal and the outside of theear due to poor occlusion of the ear by the personal audio device. Gapsbetween the ear or head and the personal audio device may allow sound totravel between the ear canal and the outside of the ear canal. Such anacoustic path may affect ANC and thus be used to adapt the probe signalfor ANC. Additionally, the ear canal response may also affect ANC. Thus,by estimating a response to the probe signal which takes into accountthe leakage path in addition to the ear canal impulse response, animproved estimation of ANC parameters may be achieved.

To that end, the estimated response may be used to adapt the probesignal for the ANC. The ANC may comprise feedback ANC or feedforward ANCor a combination of both feedback ANC and feedforward ANC. Feedback andfeedforward ANC are described in more detail below.

In some embodiments, the acoustic process comprises an ear biometricprocess. In which case, identifying the system may comprise obtaining anear canal response of the user's ear. The indication of background noisemay be obtained from an internal microphone of a personal audio deviceproximate the user's ear. The internal microphone may be used to pick upenvironmental noise which has reached the ear canal despite the personalaudio device. The indication of background noise may be obtained fromsignals received from both the internal microphone and an externalmicrophone of the personal audio device worn by the user. For example,obtaining the indication of background noise may comprise comparing aninternal microphone signal from the internal microphone to an externalmicrophone signal at the external microphone.

In some embodiments, obtaining the indication of background noise maycomprise estimating a transfer function between the external microphonesignal and internal microphone signal, and filtering the externalmicrophone signal by the estimated transfer function.

Generating the probe signal may comprise adapting a fixed probe signalbased on the indication of background noise. For example, generating theprobe signal may comprise determining filter parameters for whiteningthe noise in the measured response due to the background noise presentat the system; and filtering the fixed probe signal using the filterparameters.

Identifying the system may comprise performing parametric modelling onthe estimated response to generate a parametric representation of theestimated response. The parametric modelling may comprise linearpredictive coding (LPC).

Identifying the system may comprise estimating a frequency spectrum ofthe system response based on parametric representation, and comparingone or more features of the frequency spectrum with one or moretemplates.

Identifying the system may comprise generating an authentication resultfor each comparison between the one or more features and the one or moretemplates. The authentication result may be generated by a machinelearning classifier trained using the one or more templates.

The method may further comprise comparing the measured response or thefrequency spectrum with the one or more templates.

According to another aspect of the disclosure, there is provided amethod of authenticating a user as an authorised user, the methodcomprising: obtaining an indication of background noise present at anear canal of the user; generating a probe signal based on theindication; applying the probe signal to the ear canal of the user;measuring sound proximate the ear canal during or after application ofthe probe signal; estimating a response of the ear canal to the probesignal based on the measured sound; and identifying the user based onthe estimated response and the probe signal, wherein the probe signalcomprises a whitening component configured to whiten the noise presentin the estimated response due to the background noise present at thesystem.

According to another aspect of the disclosure, there is provided anon-transitory machine-readable medium storing instructions which, whenexecuted by one or more processors, cause an electronic apparatus toperform the method as described above.

According to another aspect of the disclosure, there is provided anapparatus for identifying a system, the apparatus comprising: an inputfor obtaining an indication of background noise present at the system;and one or more processors configured to control the apparatus to:generate a probe signal based on the indication; apply the probe signalto the system; estimate a response of the system to the probe signal;and identify the system based on the measured response and the probesignal, wherein the probe signal comprises a whitening componentconfigured to whiten noise in the estimated response due to thebackground noise present at the system.

According to another aspect of the disclosure, there is provided anapparatus for identifying a user, the apparatus comprising: an input forobtaining an indication of background noise present at an ear canal ofthe user; and one or more processors configured to control the apparatusto: generate a probe signal based on the indication; apply the probesignal to the ear canal of the user; measure sound proximate the earcanal during or after application of the probe signal; estimate aresponse of the ear canal to the probe signal based on the measuredsound; and identify the user based on the estimated response and theprobe signal, wherein the probe signal comprises a whitening componentconfigured to whiten the noise present in the estimated response due tothe background noise present at the system.

According to another aspect of the disclosure, there is provided anapparatus or identifying a user, the apparatus comprising: an input forobtaining an indication of background noise present at an ear canal ofthe user; and one or more processors configured to control the apparatusto perform any of the methods described above.

According to another aspect of the disclosure, there is provided anelectronic device comprising one or more of the apparatuses describedabove.

Throughout this specification the word “comprise”, or variations such as“comprises” or “comprising”, will be understood to imply the inclusionof a stated element, integer or step, or group of elements, integers orsteps, but not the exclusion of any other element, integer or step, orgroup of elements, integers or steps.

BRIEF DESCRIPTION OF DRAWINGS

Embodiments of the present disclosure will now be described by way ofnon-limiting examples with reference to the drawings, in which:

FIGS. 1 a to 1 e show examples of personal audio devices;

FIG. 2 shows an arrangement according to embodiments of the disclosure;

FIG. 3 shows a system according to embodiments of the disclosure;

FIG. 4 graphically illustrates a measured frequency response of an earcanal to an acoustic probe in the presence and absence of noise;

FIG. 5 is a graph showing probability distribution functions fordetection statistics representing similarities between ear canalresponses and stored templates;

FIG. 6 is a block diagram of a process which may be implemented by thesystem shown in FIG. 3 ;

FIG. 7 is a graph comparing measured ear canal impulse responses inresponse to different probe signals;

FIG. 8 is a graph comparing measured ear canal frequency responses inresponse to different probe signals; and

FIG. 9 is a block diagram of a process which may be implemented by thesystem shown in FIG. 3 .

DESCRIPTION OF EMBODIMENTS

Embodiments of the disclosure relate to methods, apparatus and systemsfor biometric processes, and particularly to methods, apparatus andsystems for improving biometric processes involving the measuredresponse of a user's ear to an acoustic stimulus.

FIG. 1 a shows a schematic diagram of a user's ear, comprising the(external) pinna or auricle 12 a, and the (internal) ear canal 12 b. Apersonal audio device 20 comprising a circum-aural headphone is worn bythe user over the ear. The headphone comprises a shell whichsubstantially surrounds and encloses the auricle 12 a, so as to providea physical barrier between the user's ear and the external environment.Cushioning or padding may be provided at an edge of the shell, so as toincrease the comfort of the user, and also the acoustic coupling betweenthe headphone and the user's skin (i.e. to provide a more effectivebarrier between the external environment and the user's ear).

The headphone comprises one or more loudspeakers 22 positioned on aninternal surface of the headphone and arranged to generate acousticsignals towards the user's ear and particularly the ear canal 12 b. Theheadphone further comprises one or more microphones 24, also positionedon the internal surface of the headphone, arranged to detect acousticsignals within the internal volume defined by the headphone, the auricle12 a and the ear canal 12 b.

The headphone may be able to perform active noise cancellation, toreduce the amount of noise experienced by the user of the headphone.Active noise cancellation operates by detecting a noise (i.e. with amicrophone) and generating a signal (i.e. with a loudspeaker) that hasthe same amplitude as the noise signal but is opposite in phase. Thegenerated signal thus interferes destructively with the noise and solessens the noise experienced by the user. Active noise cancellation mayoperate on the basis of feedback signals, feedforward signals, or acombination of both. Feedforward active noise cancellation utilizes oneor more microphones on an external surface of the headphone, operativeto detect the environmental noise before it reaches the user's ear. Thedetected noise is processed quickly, and the cancellation signalgenerated so as to match the incoming noise as it arrives at the user'sear. Feedback active noise cancellation utilizes one or more errormicrophones positioned on the internal surface of the headphone,operative to detect the combination of the noise and the audio playbacksignal generated by the one or more loudspeakers. This combination isused in a feedback loop, together with knowledge of the audio playbacksignal, to adjust the cancelling signal generated by the loudspeaker andso reduce the noise. The microphone 24 shown in FIG. 1 a may thereforeform part of an active noise cancellation system, for example, as anerror microphone. Adaptive ANC systems require estimation of the earcanal response system (sometimes referred to as a secondary path). Thesecondary path response (the ear canal response system for the purposeANC) is a function of both the physiological structure of ear canal andthe quality of fit of the headset implementing ANC. The playback signalcan be adaptively modified based on the prevailing noise conditions.

FIG. 1 b shows an alternative personal audio device 30, comprising asupra-aural headphone. The supra-aural headphone does not surround orenclose the user's ear, but rather sits on the auricle 12 a. Theheadphone may comprise a cushion or padding to lessen the impact ofenvironmental noise. As with the circum-aural headphone shown in FIG. 1a , the supra-aural headphone comprises one or more loudspeakers 32 andone or more microphones 34. The loudspeaker(s) 32 and the microphone(s)34 may form part of an active noise cancellation system, with themicrophone 34 serving as an error microphone.

FIG. 1 c shows a further alternative personal audio device 40,comprising an intra-concha headphone (or earphone). In use, theintra-concha headphone sits inside the user's concha cavity. Theintra-concha headphone may fit loosely within the cavity, allowing theflow of air into and out of the user's ear canal 12 b.

As with the devices shown in FIGS. 1 a and 1 b , the intra-conchaheadphone comprises one or more loudspeakers 42 and one or moremicrophones 44, which may form part of an active noise cancellationsystem.

FIG. 1 d shows a further alternative personal audio device 50,comprising an in-ear headphone (or earphone), insert headphone, or earbud. This headphone is configured to be partially or totally insertedwithin the ear canal 12 b and may provide a relatively tight sealbetween the ear canal 12 b and the external environment (i.e. it may beacoustically closed or sealed). The headphone may comprise one or moreloudspeakers 52 and one or more microphones 54, as with the otherdevices described above, and these components may form part of an activenoise cancellation system.

As the in-ear headphone may provide a relatively tight acoustic sealaround the ear canal 12 b, external noise (i.e. coming from theenvironment outside) detected by the microphone 54 is likely to be low.

FIG. 1 e shows a further alternative personal audio device 60, which isa mobile or cellular phone or handset. The handset 60 comprises one ormore loudspeakers 62 for audio playback to the user, and one or moremicrophones 64 which are similarly positioned.

In use, the handset 60 is held close to the user's ear so as to provideaudio playback (e.g. during a call). While a tight acoustic seal is notachieved between the handset 60 and the user's ear, the handset 60 istypically held close enough that an acoustic stimulus applied to the earvia the one or more loudspeakers 62 generates a response from the earwhich can be detected by the one or more microphones 64. As with theother devices, the loudspeaker(s) 62 and microphone(s) 64 may form partof an active noise cancellation system.

All of the personal audio devices described above thus provide audioplayback to a single user in use. Each device comprises one or moreloudspeakers and one or more microphones, which may be utilized togenerate biometric data related to the frequency response of the user'sear. The loudspeaker is operable to generate an acoustic stimulus, oracoustic probing wave, towards the user's ear, and the microphone isoperable to detect and measure a response of the user's ear to theacoustic stimulus, e.g. to measure acoustic waves reflected from the earcanal or the pinna. The acoustic stimulus may be sonic (for example inthe audio frequency range of say 20 Hz to 20 kHz) or ultra-sonic (forexample greater than 20 kHz or in the range 20 kHz to 50 kHz) ornear-ultrasonic (for example in the range 15 kHz to 25 kHz) or subsonicin frequency. In some examples the microphone signal may be processed tomeasure received signals of the same frequency as that transmitted.

Another biometric marker may comprise otoacoustic noises emitted by thecochlear in response to the acoustic stimulus waveform. The otoacousticresponse may comprise a mix of the frequencies in the input waveform.For example, if the input acoustic stimulus consists of two tones atfrequencies f1 and f2, the otoacoustic emission may include a componentat frequency 2*f1-f2. The relative power of frequency components of theemitted waveform has been shown to be a useful biometric indicator. Insome examples therefore the acoustic stimulus may comprise tones of twoor more frequencies and the amplitude of mixing products at sums ordifferences of integer-multiple frequencies generated by otoacousticemissions from the cochlear may be measured. Alternatively, otoacousticemissions may be stimulated and measured by using stimulus waveformscomprising fast transients, e.g. clicks.

Depending on the construction and usage of the personal audio device,the measured response may comprise user-specific components, i.e.biometric data relating to the auricle 12 a, the ear canal 12 b, or acombination of both the auricle 12 a and the ear canal 12 b. Forexample, the circum-aural headphones shown in FIG. 1 a will generallyacquire data relating to the auricle 12 a and potentially also the earcanal 12 b. The insert headphones shown in FIG. 1 d will generallyacquire data relating only to the ear canal 12 b.

One or more of the personal audio devices described above (or rather,the microphones within those devices) may be operable to detectbone-conducted voice signals from the user. That is, as the user speaks,sound is projected away from the user's mouth through the air. However,acoustic vibrations will also be carried through part of the user'sskeleton or skull, such as the jawbone. These vibrations may be coupledto the ear canal 12 b through the jaw or some other part of the user'sskeleton or skull and detected by the microphone. Lower frequency soundstend to experience a stronger coupling than higher frequency sounds, andvoiced speech (i.e. that speech or those phonemes generated while thevocal cords are vibrating) is coupled more strongly via bone conductionthan unvoiced speech (i.e. that speech or those phonemes generated whilethe vocal cords are not vibrating). The in-ear headphone 50 may beparticularly suited to detecting bone-conducted speech owing to thetight acoustic coupling around the ear canal 12 b.

All of the devices shown in FIGS. 1 a to 1 e and described above may beused to implement aspects of the disclosure.

FIG. 2 shows an arrangement 200 according to embodiments of thedisclosure. The arrangement 200 comprises a personal audio device 202and a biometric system 204. The personal audio device 202 may be anydevice which is suitable for, or configurable to provide audio playbackto a single user. The personal audio device 202 generally comprises oneor more loudspeakers, and one or more microphones which, in use, arepositioned adjacent to or within a user's ear. The personal audio device202 may be wearable and comprise headphones for each of the user's ears.Alternatively, the personal audio device 202 may be operable to becarried by the user and held adjacent to the user's ear or ears duringuse. The personal audio device 202 may comprise headphones or a mobilephone handset, as described above with respect to any of FIGS. 1 a to 1e.

The biometric system 204 is coupled to the personal audio device 202 andoperative to control the personal audio device 202 to acquire biometricdata which is indicative of the individual using the personal audiodevice 202.

The personal audio device 202 thus generates an acoustic stimulus forapplication to the user's ear and detects or measures the response ofthe ear to the acoustic stimulus. For example, the acoustic stimulus maybe in the sonic range, or ultra-sonic. In some embodiments, the acousticstimulus may have a flat frequency spectrum over a relevant frequencyrange or be pre-processed in such a way that those frequencies thatallow for a good discrimination between individuals are emphasized (i.e.have a higher amplitude than other frequencies). The measured responsecorresponds to the reflected signal received at the one or moremicrophones, with certain frequencies being reflected at higheramplitudes than other frequencies owing to the particular response ofthe user's ear.

The biometric system 204 may send suitable control signals to thepersonal audio device 202, so as to initiate the acquisition ofbiometric data, and receive data from the personal audio device 202corresponding to the measured response. The biometric system 204 isoperable to extract one or more features from the measured response andutilize those features as part of a biometric process.

Some examples of suitable biometric processes include biometricenrolment and biometric authentication. Enrolment comprises theacquisition and storage of biometric data which is characteristic of anindividual. In the present context, such stored data may be known as an“ear print”. Authentication (sometimes referred to as verification)comprises the acquisition of biometric data from an individual, and thecomparison of that data to the stored ear prints of one or more enrolledor authorised users. A positive comparison (i.e. a determination thatthe acquired data matches or is sufficiently close to a stored earprint) results in the individual being authenticated. For example, theindividual may be permitted to carry out a restricted action, or grantedaccess to a restricted area or device. A negative comparison (i.e. adetermination that the acquired data does not match or is notsufficiently close to a stored ear print) results in the individual notbeing authenticated. For example, the individual may not be permitted tocarry out the restricted action or granted access to the restricted areaor device.

According to embodiments of the disclosure, the personal audio device202 is further operable to determine whether a signal to noise ratio(SNR) of the response signal is adequate for performing a biometricprocess, such as feature extraction for authentication. In response todetermining that the SNR of the response signal is inadequate, thepersonal audio device 202 may be operable to modify one or moreproperties of the acoustic stimulus to improve the SNR of the responsesignal, as discussed in more detail below.

The biometric system 204 may, in some embodiments, form part of thepersonal audio device 202 itself. Alternatively, the biometric system204 may form part of an electronic host device (e.g. an audio player) towhich the personal audio device 202 is coupled, through wires orwirelessly. In yet further embodiments, operations of the biometricsystem 204 may be distributed between circuitry in the personal audiodevice 202 and the electronic host device.

FIG. 3 shows a system 300 according to embodiments of the disclosure.

The system 300 comprises processing circuitry 322, which may compriseone or more processors, such as a central processing unit or anapplications processor (AP), or a digital signal processor (DSP).

The one or more processors may perform methods as described herein onthe basis of data and program instructions stored in memory 324. Memory324 may be provided as a single component or as multiple components orco-integrated with at least some of processing circuitry 322.Specifically, the methods described herein can be performed inprocessing circuitry 322 by executing instructions that are stored innon-transient form in the memory 324, with the program instructionsbeing stored either during manufacture of the system 300 or personalaudio device 202 or by upload while the system or device is in use.

The processing circuitry 322 comprises a stimulus generator module 303which is coupled directly or indirectly to an amplifier 304, which inturn is coupled to a loudspeaker 306.

The stimulus generator module 303 generates an electrical audio signaland provides the electrical audio signal to the amplifier 304, whichamplifies it and provides the amplified signal to the loudspeaker 306.The loudspeaker 306 generates a corresponding acoustic signal which isoutput to the user's ear (or ears). The audio signal may be sonic orultra-sonic, for example. The audio signal may have a flat frequencyspectrum or be pre-processed in such a way that those frequencies thatallow for a good discrimination between individuals are emphasized (i.e.have a higher amplitude than other frequencies).

As noted above, the audio signal may be output to all or a part of theuser's ear (i.e. the auricle 12 a or the ear canal 12 b). The audiosignal is reflected off the ear, and the reflected signal (or echosignal) is detected and received by a microphone 308. The reflectedsignal thus comprises data, which is characteristic of the individual'sear, and suitable for use as a biometric.

The reflected signal is passed from the microphone 308 to ananalogue-to-digital converter (ADC) 310, where it is converted from theanalogue domain to the digital domain. Of course, in alternativeembodiments the microphone 308 may be a digital microphone and produce adigital data signal (which does not therefore require conversion to thedigital domain).

The signal is detected by the microphone 308 in the time domain. Thefeatures extracted for the purposes of the biometric process may be inthe time domain. However, in some embodiments, the features extractedfor the purposes of the biometric process may be in the frequency domain(in that it is the frequency response of the user's ear which ischaracteristic). The system 300 may therefore comprise a Fouriertransform module 312, which converts the reflected signal to thefrequency domain. For example, the Fourier transform module 312 mayimplement a fast Fourier transform (FFT).

The system 300 may further comprise an additional microphone 330, and anassociated analogue-to-digital converter (ADC) 332 where necessary. Themicrophone 330 may be an external or out-of-ear microphone, which may beused for noise signal determinations, for example, as discussed in moredetail below. An electrical audio signal generated by the additionalmicrophone 330 may be provided (optionally via the ADC 332) to the FFTmodule 312 or to another FFT module (not shown). Equally, the electricalaudio signal may be provided directly to the control module 302 in otherembodiments.

The transformed signal from the microphone 308 is then passed to afeature extract module 314, which extracts one or more features of thetransformed signal for use in a biometric process (e.g. biometricenrolment, biometric authentication, etc.). For example, the featureextract module 314 may extract the resonant frequency of the user's ear.For example, the feature extract module 314 may extract one or more melfrequency cepstrum coefficients. Alternatively, the feature extractmodule 314 may determine the frequency response of the user's ear at oneor more predetermined frequencies, or across one or more ranges offrequencies.

The extracted feature(s) are passed to a biometric module 316, whichperforms a biometric process on them. For example, the biometric module316 may perform a biometric enrolment, in which the extracted features(or parameters derived therefrom) are stored as part of biometrictemplate data 318 which is characteristic of the individual (i.e. as anear print). The biometric template data 318 may be stored within thesystem 300 or remote from the system 300 (and accessible securely by thebiometric module 316). In another example, the biometric 316 may performa biometric authentication, and compare the one or more extract featuresto corresponding features stored in the biometric template data 318 (ormultiple stored template ear prints). The biometric template data 318may comprise template data, representations or ear prints of enrolledusers. Additionally or alternatively the biometric template data 318 maycomprise data representing multiple users, for example a subset of thegeneral population. This template data 318 may also be accessible by thecontrol module 302 for use in generating an acoustic stimulus as isdescribed in more detail below.

The biometric module 316 may generate a biometric result (which may bethe successful or unsuccessful generation of an ear print, as well assuccessful or unsuccessful authentication) and outputs the result tocontrol module 302.

Thus in some embodiments the feature extract module 314 may be designedwith foreknowledge of the nature of the stimulus, for example knowingthe spectrum of the applied stimulus signal, so that the response ortransfer function may be appropriately normalised. In other embodimentsthe feature extract module 314 may comprise a second input (not shown)to monitor the stimulus and hence provide the feature extract module 314with information about the stimulus signal or its spectrum so that thefeature extract module 314 may calculate the transfer function from thestimulus waveform stimulus to received acoustic waveform from which itmay derive the desired feature parameters. In the latter case, thestimulus signal may also pass to the feature extract module 314 via theFFT module 312.

It has been found that the above process operates efficiently whenenvironmental noise in the vicinity of the microphone 308 and the ear ofthe user is absent or low. However, as the level or environmental noiseincreases, the response to the acoustic stimulus measured by themicrophone 308, becomes increasingly corrupted. This lack of robustnessleads to errors and uncertainty in the biometric result generated by thesystem 300. It has also been found that different types of environmentalnoise corrupt the measured response to different degrees.

FIG. 4 is a graph of the measured frequency response of the ear canal ofa user in the presence of no noise (clean), natural noise (nature), andhousehold noise (household). The presence of both natural and householdnoise leads to spurious spectral artefacts 402, 404, 406, 408 in themeasured frequency response. It can also be seen that the artefacts 406,408 present in the frequency response measured in the presence ofhousehold noise are more pronounced than those artefacts 402, 404present in the frequency response measured in the presence of the noiseof nature.

FIG. 5 is a graph showing probability density functions of detectionstatistics for an enrolled user 502 and an imposter 504 in two differentnoise conditions. The detection statistic (x-axis) represents thesimilarity between the ear canal response estimated from the receivedaudio signal and stored template data 318. A statistic of 1 means that ameasured response is identical to a stored template.

It can be seen that the detection statistic value is substantiallyhigher for the enrolled user 502 than the imposter 504. To achieve goodauthentication performance, the detection statistic distribution for theenrolled user 502 and the imposter 504 should be well separated. Theensures that a threshold can be set (substantially between the enrolledand imposter detection statistics) that minimizes false rejection ofenrolled users whilst also minimising false accepting of imposters.

In the example shown in FIG. 5 , in the presence of nature noise, thedetection statistic distribution for the enrolled user overlaps to agreater extent with the imposter distribution when compared to similardistributions in the presence of household noise. This may result inreduced authentication performance, for example for a fixed false acceptrate (FAR). It is preferable that the distribution is invariant todifferent noise types such that a single threshold can be set thatimproves the likelihood of similar authentication performance fordifferent noise types.

Thus, it can be seen that noise present in estimated system responses,such as an ear canal impulse response (ECIR), can result in noisedependent authentication performance variation.

Embodiments of the present disclosure aim to address or at leastameliorate problems associated with the corruption of estimations of earcanal impulse and frequency response by additive noise. In embodimentsof the present disclosure, probe signals applied to systems such as anear canal may be adapted such that their application to the ear canalleads to whitening of the environmental noise picked up duringestimation of the ear canal response to the probe signal. In someembodiments, a fixed probe signal is adaptively filtered based on anestimate of the environmental noise affecting the ear canal (or othersystem under test). A filter characteristic of the filter used to adaptthe fixed probe signal may be generated based on a prevailing noisecondition. This condition may be measured at one or more transducersproximate the system under test (e.g., the ear canal). By whiteningnoise present in the estimate of the system response to the probesignal, through appropriate adaptation of the probe signal, such noisemay be substantially regularized. This in turn leads to improvements inthe robustness of authenticating the identity of the system, such as thecharacteristic of an ear canal (or other system) under test.

In addition, embodiments of the present disclosure provide noise-robustmethods of feature extraction in which resonant and anti-resonantregions of a system response are parametrically modelled. Usefulfeatures may be extracted by parametric models and the model parametersaugmented with the estimated system impulse response. The resultantextracted feature sets, which include parametrically modelledparameters, tend to be more robust to different noise conditions as wellas different noise types.

In some embodiments, synergy may be achieved by combining the whiteningof noise in system estimates with parametric feature extraction. This isbecause whitened noise in the measured system response may besubstantially removed in subsequent parametric feature extraction.

Taking the above into consideration, in the example of ear biometrics,the control module 302 may be configured to control the stimulusgenerator module 303 to generate an acoustic stimulus having theproperties described above.

FIG. 6 is a block diagram of a process 600 which may be implemented bythe processing circuitry 322 for generating a probe signal or stimulushaving the characteristics described above. For context, the process 600is described below as being implemented by the system 300 shown in FIG.3 . In other embodiments, however, the process 600 may be implemented inanother system (not shown) which may be proximate to or remote from thesystem 300. For example, one or more steps in the process 600 may beimplemented in the cloud. The generated acoustic stimulus may be used bythe system 300 or other systems for use in a biometric process, such asbiometric enrolment or authentication, an example of which is describedabove. For example, where a user has multiple personal audio devices,the acoustic stimulus by the process 600 may be utilised by each ofthose devices for one or more biometric processes. Various steps of theprocess 600 described below may be implemented by different modules ofthe system 300 or other systems which may themselves be disparate fromone another.

It will be understood that when a probe signal A is applied to the earcanal 12 b of a user, the signal y measured at the microphone 330 of thesystem 300 comprises both external noise and the response to the probesignal A. The signal y may be defined as follows:

y=Ah+b

Where h is the response of the ear canal.

A least square estimate of h may be given by the following equation.

$\overset{\hat{}}{h} = {{\left( {A^{T}A} \right)^{- 1}A^{T}y} = {{{\left( {A^{T}A} \right)^{- 1}A^{T}Ah} + {\left( {A^{T}A} \right)^{- 1}A^{T}b}} = {{hest} + {Wb}}}}$

Where:

hest=(A ^(T) A)⁻¹ A ^(T) Ah

Wb=(A ^(T) A)⁻¹ A ^(T) b

Hence, the least square estimate of the canal response h is corrupted byadditive noise Wb.

Taking the above into account, the process 600 aims to whiten theadditive noise Wb so as to remove or reduce its variance with respect totime (i.e., regularizing the additive noise). In doing so, subsequentprocessing of the signal y received at the microphone 300 can moreeasily remove the noise. The effect of the whitened noise on theauthentication performance can be minimized by using parametricmodelling. Model parameters may be further expanded to derive features.The combined regularization of noise in the measured system response onthe one hand and parametric modelling on the other hand, leads toimproved authentication performance.

Referring to FIG. 6 , at step 602, the processing circuitry 322 mayobtain an estimate of the external or environmental noise b present inthe ear canal. In some embodiments, this noise estimate may be obtainedby sampling the microphone 308 which may be located proximate the earcanal. It is preferable that any such sample is obtained during periodsin which no sound is being produced by the loudspeaker 306 to minimizeinterference with the measured noise. In other embodiments, the noiseestimate may be obtained by comparing the signal received at themicrophone 308 with a signal received at the additional microphone 330which is external to the personal audio device. For example, a transferfunction between the microphone 308 and the additional microphone 330may be estimated. This transfer function may then be used to generate anestimate of the environmental noise b at microphone 308 by filtering themicrophone 330 signal with the estimated transfer function.

At step 604 a set of filter parameters may be generated based on theestimated noise b and the fixed (unfiltered) probe signal. The filterparameters may be continuously or periodically updated based on thenoise estimate b.

The filter parameters may be chosen so as to whiten the noise componentWb as estimated using least squared estimation, i.e.:

Wb=(A ^(T) A)⁻¹ A ^(T) b

Specifically, filter parameters are chosen to produce a probe signal Awhich, in combination with the noise estimate b leads to the expression(A^(T)A)⁻¹A^(T)b resembling white noise or near-white noise (or in otherwords having a flat or near flat frequency response). In anotherembodiment, characteristics of the whitening filter are derived byperforming spectral analysis on the filtered noise estimate(A^(T)A)⁻¹A^(T)b to generate a spectral estimate. The whitening filtermay be designed to have a transfer function which approximates theinverse of the spectral estimate. For example, low order linearprediction analysis may be performed and the all-pole LPC modelcoefficients may be used as inputs to the whitening filter.

The filter parameters may then be used at step 606 to augment the fixedprobe signal and generate a filtered probe signal A for application tothe ear canal 12 b. Whilst in the embodiment shown in FIG. 6 a fixedprobe signal is adapted using an adaptive filter, in other embodimentsthe probe signal A may be generated from scratch based on the noiseestimation b.

FIG. 7 is a graph comparing the measured ear canal impulse response to afixed probe signal and to a probe signal adapted to whiten environmentalnoise present at the ear canal. It can be seen that whilst noise isstill present in the measured impulse response to the adaptive probesignal, the noise is less fluctuating over time.

FIG. 8 shows the corresponding ear canal frequency response to the samefixed probe signal and the same adaptive probe signal. It can be seenthat spurious errors 802, 804 due to environmental noise which arepresent in the frequency response to the fixed probe signal are notpresent in the response to the adaptive probe signal. Thus, theaugmented probe signal acts to remove spurious errors associated withenvironmental noise present during measurements of ear canal response.

Various method exist for processing system responses, such as ear canalimpulse and frequency responses to remove noise. The inventors havefound that parametric modelling is particularly effective in extractingfeatures from system responses comprising time-invariant noise (such asthe ear canal response measured in response to the adapted probe signaldescribed above).

FIG. 9 is a block diagram of a process 900 which may be implemented bythe processing circuitry 322 for processing a system response, such asan ear canal impulse response measured at the transducer 308 of thesystem 300.

At step 902, an initial ear canal impulse response ECIR is estimatedusing conventional correlation techniques known in the art. Exampletechniques include least mean squares (LMS), least squares, andrecursive least squares (RLS). The ECIR may be estimated using an earcanal model based on the received audio signal y[n] from the transducer330 and the probe signal A applied to the ear canal.

At step 904, parametric modelling may performed on the estimated ECIRh[n] to refine the estimate. A parametric representation Para of theECIR h[f] is generated at this step. Such modelling may comprise usinglinear predictive coding (LPC) in some embodiments. In some embodiments,an all pole model may be used to model the parameters based on theestimated ECIR. Additionally or alternatively, a pole-zero model may beused. To derive pole-zero modelling parameters, Pade approximation orProny's method may be used.

The parametric representation captures correlated information in the earcanal response, such as resonance or anti-resonance. Such information isexemplified by peaks and troughs in the frequency response curves shownin FIGS. 4 and 8 .

The parametric representation Para may comprise parameters which maythen be used to generate a frequency spectrum or ECFR. Thus, at step 906spectral processing may be performed to generate such a frequencyspectrum.

Where the parametric representation Para is generated using linearprediction, the Nth order LPC model frequency spectrum can be writtenas:

${H_{LP}^{ECIR}(z)} = \frac{1}{1 + {\Sigma_{k = 1}^{N}a_{k}z^{- k}}}$

Where a_(k) is a parameter of the parametric representation Paracalculated during the parametric modelling 904.

Where the parametric representation Para is generated using pole-zeromodelling, the pole-zero model frequency spectrum can be written as:

${H_{PZ}^{ECIR}(z)} = \frac{\Sigma_{k = 0}^{M}b_{m}z^{- m}}{1 + {\Sigma_{k = 1}^{N}a_{k}z^{- k}}}$

Where a_(k) and b_(m) are parameters of the parametric representationPara calculated during the parametric modelling 904.

The resultant spectral representation obtained from spectral processingof the parametric model parameters output at step 906 has a smootherspectrum whilst providing robustness to noise. This in turn leads tomore accurate verification/authentication. Further, when coupled withthe techniques described above for probe signal generation, the estimateof ECIR is even more robust when compared to using conventionalclassification techniques with an adaptive probe signal. This is becausethe frequency spectrum of the ear canal response will be substantiallyabsent of or have reduced additive modelling error since the modellingerror due to the additive noise has been whitened (i.e. temporallyuncorrelated).

Optionally, in addition to generating a frequency spectrum of the earcanal response, e.g., the ECFR of the system, the spectral processing atstep 906 may include feature extraction. Such feature extraction may beperformed by the feature extract module 314. One or more features of thespectrum may be extracted. Such extraction may be performed for use in abiometric process (e.g. biometric enrolment, biometric authentication,etc.). For example, the feature extract module 314 may extract theresonance or antiresonance of the system (e.g. peaks and troughs). Forexample, the feature extract module 314 may extract one or more melfrequency cepstrum coefficients. Additionally or alternatively, thefeature extract module 314 may determine the frequency response of theuser's ear at one or more predetermined frequencies, or across one ormore ranges of frequencies.

At step 908, the spectrum and/or the one or more extracted features maythen be may then be classified. For example, the spectrum and/or the oneor more extracted features may be compared with a stored authenticationtemplate, such as the template data 318. A determination as to whetherthe modelled system (e.g. ear canal) is authenticated may then depend ona similarity between the stored template and the parametricrepresentation.

Optionally, the estimated ECIR may be combined with the spectrum and/orone or more extracted features for use during classification, as denotedby the broken line in FIG. 9 to further improve performance of theprocess 900.

As discussed throughout the present disclosure, whilst the examplesdescribed herein in relation to the ear canal, the present disclosure isnot limited to such systems. The concepts described herein can beapplied to any system which is subject to externally applied noise andwhich have an unknown transfer function. For example, embodiments of thepresent disclosure may equally be applied to account for environmentalnoise present during active noise cancellation in a personal audiodevice, such as those described above.

The skilled person will recognise that some aspects of theabove-described apparatus and methods may be embodied as processorcontrol code, for example on a non-volatile carrier medium such as adisk, CD- or DVD-ROM, programmed memory such as read only memory(Firmware), or on a data carrier such as an optical or electrical signalcarrier. For many applications embodiments of the invention will beimplemented on a DSP (Digital Signal Processor), ASIC (ApplicationSpecific Integrated Circuit) or FPGA (Field Programmable Gate Array).Thus the code may comprise conventional program code or microcode or,for example code for setting up or controlling an ASIC or FPGA. The codemay also comprise code for dynamically configuring re-configurableapparatus such as re-programmable logic gate arrays. Similarly the codemay comprise code for a hardware description language such as Verilog TMor VHDL (Very high speed integrated circuit Hardware DescriptionLanguage). As the skilled person will appreciate, the code may bedistributed between a plurality of coupled components in communicationwith one another. Where appropriate, the embodiments may also beimplemented using code running on a field-(re)programmable analoguearray or similar device in order to configure analogue hardware.

Note that as used herein the term module shall be used to refer to afunctional unit or block which may be implemented at least partly bydedicated hardware components such as custom defined circuitry and/or atleast partly be implemented by one or more software processors orappropriate code running on a suitable general purpose processor or thelike. A module may itself comprise other modules or functional units. Amodule may be provided by multiple components or sub-modules which neednot be co-located and could be provided on different integrated circuitsand/or running on different processors.

Embodiments may be implemented in a host device, especially a portableand/or battery powered host device such as a mobile computing device forexample a laptop or tablet computer, a games console, a remote controldevice, a home automation controller or a domestic appliance including adomestic temperature or lighting control system, a toy, a machine suchas a robot, an audio player, a video player, or a mobile telephone forexample a smartphone.

As used herein, when two or more elements are referred to as “coupled”to one another, such term indicates that such two or more elements arein electronic communication or mechanical communication, as applicable,whether connected indirectly or directly, with or without interveningelements.

This disclosure encompasses all changes, substitutions, variations,alterations, and modifications to the example embodiments herein that aperson having ordinary skill in the art would comprehend. Similarly,where appropriate, the appended claims encompass all changes,substitutions, variations, alterations, and modifications to the exampleembodiments herein that a person having ordinary skill in the art wouldcomprehend. Moreover, reference in the appended claims to an apparatusor system or a component of an apparatus or system being adapted to,arranged to, capable of, configured to, enabled to, operable to, oroperative to perform a particular function encompasses that apparatus,system, or component, whether or not it or that particular function isactivated, turned on, or unlocked, as long as that apparatus, system, orcomponent is so adapted, arranged, capable, configured, enabled,operable, or operative. Accordingly, modifications, additions, oromissions may be made to the systems, apparatuses, and methods describedherein without departing from the scope of the disclosure. For example,the components of the systems and apparatuses may be integrated orseparated. Moreover, the operations of the systems and apparatusesdisclosed herein may be performed by more, fewer, or other componentsand the methods described may include more, fewer, or other steps.Additionally, steps may be performed in any suitable order. As used inthis document, “each” refers to each member of a set or each member of asubset of a set.

Although exemplary embodiments are illustrated in the figures anddescribed below, the principles of the present disclosure may beimplemented using any number of techniques, whether currently known ornot. The present disclosure should in no way be limited to the exemplaryimplementations and techniques illustrated in the drawings and describedabove.

Unless otherwise specifically noted, articles depicted in the drawingsare not necessarily drawn to scale.

All examples and conditional language recited herein are intended forpedagogical objects to aid the reader in understanding the disclosureand the concepts contributed by the inventor to furthering the art, andare construed as being without limitation to such specifically recitedexamples and conditions. Although embodiments of the present disclosurehave been described in detail, it should be understood that variouschanges, substitutions, and alterations could be made hereto withoutdeparting from the spirit and scope of the disclosure.

Although specific advantages have been enumerated above, variousembodiments may include some, none, or all of the enumerated advantages.Additionally, other technical advantages may become readily apparent toone of ordinary skill in the art after review of the foregoing figuresand description.

It should be noted that the above-mentioned embodiments illustraterather than limit the invention, and that those skilled in the art willbe able to design many alternative embodiments without departing fromthe scope of the appended claims. The word “comprising” does not excludethe presence of elements or steps other than those listed in a claim,“a” or “an” does not exclude a plurality, and a single feature or otherunit may fulfil the functions of several units recited in the claims.Any reference numerals or labels in the claims shall not be construed soas to limit their scope.

As used herein, when two or more elements are referred to as “coupled”to one another, such term indicates that such two or more elements arein electronic communication or mechanical communication, as applicable,whether connected indirectly or directly, with or without interveningelements.

This disclosure encompasses all changes, substitutions, variations,alterations, and modifications to the example embodiments herein that aperson having ordinary skill in the art would comprehend. Similarly,where appropriate, the appended claims encompass all changes,substitutions, variations, alterations, and modifications to the exampleembodiments herein that a person having ordinary skill in the art wouldcomprehend. Moreover, reference in the appended claims to an apparatusor system or a component of an apparatus or system being adapted to,arranged to, capable of, configured to, enabled to, operable to, oroperative to perform a particular function encompasses that apparatus,system, or component, whether or not it or that particular function isactivated, turned on, or unlocked, as long as that apparatus, system, orcomponent is so adapted, arranged, capable, configured, enabled,operable, or operative. Accordingly, modifications, additions, oromissions may be made to the systems, apparatuses, and methods describedherein without departing from the scope of the disclosure. For example,the components of the systems and apparatuses may be integrated orseparated. Moreover, the operations of the systems and apparatusesdisclosed herein may be performed by more, fewer, or other componentsand the methods described may include more, fewer, or other steps.Additionally, steps may be performed in any suitable order. As used inthis document, “each” refers to each member of a set or each member of asubset of a set.

Although exemplary embodiments are illustrated in the figures anddescribed below, the principles of the present disclosure may beimplemented using any number of techniques, whether currently known ornot. The present disclosure should in no way be limited to the exemplaryimplementations and techniques illustrated in the drawings and describedabove.

Unless otherwise specifically noted, articles depicted in the drawingsare not necessarily drawn to scale.

All examples and conditional language recited herein are intended forpedagogical objects to aid the reader in understanding the disclosureand the concepts contributed by the inventor to furthering the art, andare construed as being without limitation to such specifically recitedexamples and conditions. Although embodiments of the present disclosurehave been described in detail, it should be understood that variouschanges, substitutions, and alterations could be made hereto withoutdeparting from the spirit and scope of the disclosure.

Although specific advantages have been enumerated above, variousembodiments may include some, none, or all of the enumerated advantages.Additionally, other technical advantages may become readily apparent toone of ordinary skill in the art after review of the foregoing figuresand description.

To aid the Patent Office and any readers of any patent issued on thisapplication in interpreting the claims appended hereto, applicants wishto note that they do not intend any of the appended claims or claimelements to invoke 35 U.S.C. § 112(f) unless the words “means for” or“step for” are explicitly used in the particular claim.

1. A method of identifying a system, the method comprising: obtaining anindication of background noise present at the system; generating a probesignal based on the indication; applying the probe signal to the system;estimating a response of the system to the probe signal; and identifyingthe system based on the measured response and the probe signal, whereinthe probe signal comprises a whitening component configured to whitennoise in the estimated response due to the background noise present atthe system.
 2. The method of claim 1, wherein the probe signal is anacoustic stimulus for use in an acoustic process on a user and thesystem comprises an ear canal of the user.
 3. The method of claim 2,wherein the acoustic process comprises active noise cancellation (ANC).4. The method of claim 3, wherein the probe signal is an audio playbacksignal.
 5. The method of claim 4, wherein the estimated responserepresents a combination of a leakage path around a personal audiodevice worn by the user and a response of the ear canal to probe signal.6. The method of claim 4 wherein the estimated response is used to adaptthe probe signal for the ANC.
 7. The method of claim 6, wherein the ANCcomprises feedback ANC or feedforward ANC or feedback ANC andfeedforward ANC.
 8. The method of claim 2, wherein the acoustic processcomprises an ear biometric process.
 9. The method of claim 8, whereinidentifying the system comprises obtaining an ear canal response of theuser's ear.
 10. The method of claim 2, wherein the indication ofbackground noise is obtained from an internal microphone of a personalaudio device proximate the user's ear.
 11. The method of claim 10,wherein the indication of background noise is obtained from the internalmicrophone and an external microphone of the personal audio device wornby the user.
 12. The method of claim 11, wherein obtaining theindication of background noise comprises comparing an internalmicrophone signal from the internal microphone to an external microphonesignal at the external microphone.
 13. The method of claim 12, whereinobtaining the indication of background noise comprises: estimating atransfer function between the external microphone signal and internalmicrophone signal; and filtering the external microphone signal by theestimated transfer function.
 14. The method of claim 1, whereingenerating the probe signal comprises: adapting a fixed probe signalbased on the indication of background noise.
 15. The method of claim 14,wherein generating the probe signal comprises: determining filterparameters for whitening the noise in the measured response due to thebackground noise present at the system; and filtering the fixed probesignal using the filter parameters.
 16. The method of claim 1, whereinidentifying the system comprises: performing parametric modelling on theestimated response to generate a parametric representation of theestimated response.
 17. The method of claim 16, wherein the parametricmodelling comprising linear predictive coding (LPC).
 18. The method ofclaim 16, wherein identifying the system comprises: estimating afrequency spectrum of the system response based on parametricrepresentation; and comparing one or more features of the frequencyspectrum with one or more templates.
 19. The method of claim 18, whereinidentifying the system comprises: generating an authentication resultfor each comparison between the one or more features and the one or moretemplates.
 20. The method of claim 19, wherein the authentication resultis generated by a machine learning classifier trained using the one ormore templates.
 21. The method of claim 18, further comprising comparingthe measured response or the frequency spectrum with the one or moretemplates.
 22. A method of authenticating a user as an authorised user,the method comprising: obtaining an indication of background noisepresent at an ear canal of the user; generating a probe signal based onthe indication; applying the probe signal to the ear canal of the user;measuring sound proximate the ear canal during or after application ofthe probe signal; estimating a response of the ear canal to the probesignal based on the measured sound; and identifying the user based onthe estimated response and the probe signal, wherein the probe signalcomprises a whitening component configured to whiten the noise presentin the estimated response due to the background noise present at thesystem.
 23. A non-transitory machine-readable medium storinginstructions which, when executed by one or more processors, cause anelectronic apparatus to perform a method of identifying a system, themethod comprising: obtaining an indication of background noise presentat the system; generating a probe signal based on the indication;applying the probe signal to the system; estimating a response of thesystem to the probe signal; and identifying the system based on themeasured response and the probe signal, wherein the probe signalcomprises a whitening component configured to whiten noise in theestimated response due to the background noise present at the system.24. An apparatus for identifying a system, the apparatus comprising: aninput for obtaining an indication of background noise present at thesystem; and one or more processors configured to control the apparatusto: generate a probe signal based on the indication; apply the probesignal to the system; estimate a response of the system to the probesignal; and identify the system based on the measured response and theprobe signal, wherein the probe signal comprises a whitening componentconfigured to whiten noise in the estimated response due to thebackground noise present at the system.
 25. An apparatus for identifyinga user, the apparatus comprising: an input for obtaining an indicationof background noise present at an ear canal of the user; and one or moreprocessors configured to control the apparatus to: generate a probesignal based on the indication; apply the probe signal to the ear canalof the user; measure sound proximate the ear canal during or afterapplication of the probe signal; estimate a response of the ear canal tothe probe signal based on the measured sound; and identify the userbased on the estimated response and the probe signal, wherein the probesignal comprises a whitening component configured to whiten the noisepresent in the estimated response due to the background noise present atthe system.
 26. (canceled)
 27. An electronic device comprising theapparatus of claim 24.